.dockerignore

@@ -1,9 +0,0 @@
-modules/ml-pipeline/src/pipeline/data/predictions
-modules/ml-pipeline/src/pipeline/data/fit_predictions
-modules/ml-pipeline/src/pipeline/data/prepared_data
-modules/ml-pipeline/src/pipeline/data/model/allmodels
-modules/ml-pipeline/src/pipeline/metrics
-modules/ml-pipeline/src/pipeline/__pycache__
-modules/ml-pipeline/src/pipeline/.dvc
-modules/ml-pipeline/src/pipeline/analysis
-modules/ml-pipeline/src/pipeline/metrics

.github/workflows/Deploy.yml

@@ -19,8 +19,8 @@ jobs:
 
       - name: Install Serverless and plugins
         run: |
-          npm install -g serverless@^3.38.0
+          npm install -g serverless
-          npm install -g serverless-domain-manager@^7.3.8
+          npm install -g serverless-domain-manager
 
       - name: Install DVC
         run: |

.github/workflows/MLPipelinePullRequest.yml

@@ -98,16 +98,6 @@ jobs:
         git fetch --depth=1 origin ${TARGET_BRANCH}:${TARGET_BRANCH}
         dvc metrics diff --md --all ${TARGET_BRANCH} >> report.md
 
-        echo "## Scenario comparison" >> report.md
-
-        cat metrics/scenario_table.md >> report.md
-
-        echo "" >> report.md
-
-        echo "## Scenario metrics" >> report.md
-
-        cat metrics/scenario_metrics.md >> report.md
-
         cml comment create report.md
 
         # echo "## Residuals plot from model" >> report.md

MODEL_REGISTRY.md

@@ -8,25 +8,25 @@
         "active": true
     },
     "sap": {
-        "version": "v0.14.0",
+        "version": "v0.5.0",
         "stage": {
-            "dev": "v0.14.0"
+            "dev": "v0.5.0"
         },
         "registered": true,
         "active": true
     },
     "heat": {
-        "version": "v0.5.0",
+        "version": "v0.3.0",
         "stage": {
-            "dev": "v0.5.0"
+            "dev": "v0.3.0"
         },
         "registered": true,
         "active": true
     },
     "carbon": {
-        "version": "v0.5.0",
+        "version": "v0.3.0",
         "stage": {
-            "dev": "v0.5.0"
+            "dev": "v0.3.0"
         },
         "registered": true,
         "active": true

deployment/.dockerignore

@@ -1,9 +1,4 @@
-modules/ml-pipeline/src/pipeline/data/predictions
+modules/ml-pipeline/src/pipeline/data/predictions*
-modules/ml-pipeline/src/pipeline/data/fit_predictions
+modules/ml-pipeline/src/pipeline/data/prepared_data*
-modules/ml-pipeline/src/pipeline/data/prepared_data
+modules/ml-pipeline/src/pipeline/data/model/allmodels*
-modules/ml-pipeline/src/pipeline/data/model/allmodels
+modules/ml-pipeline/src/pipeline/metrics*
-modules/ml-pipeline/src/pipeline/metrics
-modules/ml-pipeline/src/__pycache__
-modules/ml-pipeline/src/.dvc
-modules/ml-pipeline/src/analysis
-modules/ml-pipeline/src/metrics

deployment/Dockerfile.prediction.lambda

@@ -9,7 +9,7 @@ ARG RUNTIME_ENVIRONMENT
 ENV RUNTIME_ENVIRONMENT=${RUNTIME_ENVIRONMENT}
 
 # Install necessary build tools - required to test locally
-RUN yum install -y gcc python3-devel gcc-c++
+RUN yum install -y gcc python3-devel
 
 # Install python packages
 COPY modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt ./requirements.txt

modules/ml-pipeline/src/.dockerignore

@@ -1,8 +1,4 @@
-pipeline/data/predictions
+pipeline/data/predictions*
-pipeline/data/fit_predictions
+pipeline/data/prepared_data/train.parquet*
-pipeline/data/prepared_data/train.parquet
+pipeline/data/model/allmodels*
-pipeline/data/fit_predictions
+pipeline/metrics*
-pipeline/data/model/allmodels
-pipeline/metrics
-pipeline/.dvc
-pipeline/analysis

modules/ml-pipeline/src/Prediction.Dockerfile

@@ -1,7 +1,7 @@
 # Dockerfile that can be used to test loading a model to generate a prediction (part of CI/CD flow)
 FROM python:3.10.12-slim
 
-RUN apt-get update && apt-get install -y libgomp1 gcc python3-dev
+RUN apt-get update && apt-get install -y libgomp1
 
 COPY pipeline/requirements/predictions/requirements.txt requirements.txt
 

modules/ml-pipeline/src/pipeline/5_generate_scenarios.py

@@ -1,162 +0,0 @@
-"""
-Fourth part of the pipeline:
-After the model is built and metrics are generated,
-we want to test this model against known scenarios
-"""
-
-import os
-import pandas as pd
-from core.interface.InterfaceModels import MLModel
-from core.interface.InterfaceDataClient import DataClient
-from core.interface.InterfaceMetrics import MLMetrics
-from configs.post_prediction_logic import post_prediction_logic
-from core.DataClient import dataclient_factory
-from core.MLModels import model_factory
-from core.MLMetrics import metrics_factory
-from core.Logger import logger
-from config import settings
-
-logger.info(f"--- Initiate Parameters ---")
-
-RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
-
-client_params = settings.client
-prepare_data_params = settings.prepare_data
-build_model_params = settings.build_model
-generate_predictions_params = settings.generate_predictions
-generate_metrics_params = settings.generate_metrics
-feature_process_params = settings.feature_processor
-scenarios_params = settings.scenarios
-
-model_filepath = build_model_params["model_save_filepath"]
-target = feature_process_params["feature_processor_config"]["target"]
-scenario_data_filepaths = scenarios_params["scenario_data_filepaths"]
-predictions_column_name = generate_predictions_params["predictions_column_name"]
-comparison_output_filepath = scenarios_params["comparison_output_filepath"]
-metrics_output_filepath = scenarios_params["metrics_output_filepath"]
-
-logger.info(f"--- Initiate MLModel ---")
-
-model = model_factory(build_model_params["model_type"])
-
-logger.info(f"--- Initiate DataClient ---")
-
-# Use data client for input and output, as we use dvc to cache later to the cloud
-input_dataclient_type = scenarios_params["input_dataclient_type"]
-input_dataclient = dataclient_factory(
-    dataclient_type=input_dataclient_type,
-    dataclient_config=client_params[input_dataclient_type],
-)
-
-output_dataclient_type = scenarios_params["output_dataclient_type"]
-output_dataclient = dataclient_factory(
-    dataclient_type=output_dataclient_type,
-    dataclient_config=client_params[output_dataclient_type],
-)
-
-logger.info(f"--- Initiate MLMetrics ---")
-
-metrics = metrics_factory(generate_metrics_params["metrics_type"])
-
-
-def generate_scenario_predictions(
-    input_dataclient: DataClient,
-    output_dataclient: DataClient,
-    model: MLModel,
-    metrics: MLMetrics,
-    model_filepath: str,
-    scenario_data_filepaths: list,
-    predictions_column_name: str,
-    comparison_output_filepath: str,
-    metrics_output_filepath: str,
-):
-    """
-    Given the new model, we generate prediction for expected scenarios
-    """
-
-    logger.info("--- Loading Scenario Data ---")
-
-    scenario_data = pd.DataFrame()
-
-    # If we have no scenario data, we can save empty dataframes
-    if scenario_data_filepaths is None:
-        logger.info("No scenario data filepaths provided")
-        output_dataclient.save_data(
-            obj=scenario_data, location=comparison_output_filepath, save_config=None
-        )
-
-        output_dataclient.save_data(
-            obj=scenario_data, location=metrics_output_filepath, save_config=None
-        )
-        return
-
-    # Can have multiple scenario data files
-    for scenario_data_filepath in scenario_data_filepaths:
-        scenario_data = pd.concat(
-            [
-                scenario_data,
-                input_dataclient.load_data(scenario_data_filepath, load_config=None),
-            ]
-        )
-
-    logger.info("--- Loading Model ---")
-
-    model.load_model(model_filepath)
-
-    logger.info("--- Generating Predictions ---")
-
-    predictions = model.predict(
-        data=scenario_data, post_prediction_logic=post_prediction_logic
-    )
-
-    logger.info("--- Generate Scenario Predicted Impact ---")
-
-    predictions_df = pd.DataFrame(predictions)
-    predictions_df.columns = [predictions_column_name]
-
-    scenario_data = pd.concat([scenario_data, predictions_df], axis=1)
-    scenario_data["predicted_impact"] = abs(
-        scenario_data[predictions_column_name] - scenario_data["sap_starting"]
-    )
-
-    logger.info("--- Generate Metrics ---")
-
-    metrics_dict = metrics.generate_metrics(
-        scenario_data["impact"], scenario_data["predicted_impact"]
-    )
-
-    metrics_df = pd.DataFrame(metrics_dict, index=[0]).T.reset_index()
-    metrics_df.columns = ["metric", "value"]
-
-    logger.info("--- Save prediction into metrics ---")
-
-    output_df = scenario_data[["uprn", "id", "impact", "predicted_impact"]]
-
-    output_dataclient.save_data(
-        obj=output_df, location=comparison_output_filepath, save_config=None
-    )
-
-    output_dataclient.save_data(
-        obj=metrics_df, location=metrics_output_filepath, save_config=None
-    )
-
-
-if __name__ == "__main__":
-
-    logger.info(f"--- {__file__} - Start! ---")
-
-    logger.info(f"--- Generate Scenario Predictions ---")
-
-    generate_scenario_predictions(
-        input_dataclient=input_dataclient,
-        output_dataclient=output_dataclient,
-        model=model,
-        metrics=metrics,
-        model_filepath=model_filepath,
-        scenario_data_filepaths=scenario_data_filepaths,
-        predictions_column_name=predictions_column_name,
-        comparison_output_filepath=comparison_output_filepath,
-        metrics_output_filepath=metrics_output_filepath,
-    )
-
-    logger.info(f"--- {__file__} - Complete! ---")

modules/ml-pipeline/src/pipeline/README.md

@@ -37,4 +37,3 @@ Workflow:
     - This experiment will have the corresponding .dvc files for the hashed model and data
 - Use version control as normal
     - git add, git commit etc
-- To revert change, use `git checkout {COMMIT_HASH}`, followed by `git switch -c {NEW_BRANCH_NAME}`

modules/ml-pipeline/src/pipeline/config.py

@@ -7,7 +7,6 @@ settings = Dynaconf(
         "./configs/settings.yaml",
         "./configs/build_model.yaml",
         "./configs/analysis.yaml",
-        "./configs/scenarios.yaml",
     ],
 )
 

modules/ml-pipeline/src/pipeline/configs/build_model.yaml

@@ -14,9 +14,8 @@ default:
       output_filepath: ./data/model/allmodels/
       problem_type: regression
       eval_metric: mean_squared_error #mean_absolute_error
-      time_limit: 1800
+      time_limit: 400
       presets: medium_quality
-      excluded_model_types: ['RF', 'CAT', 'NN_TORCH', 'KNN', 'XT']
+      excluded_model_types: ['RF', 'FASTAI', 'CAT', 'NN_TORCH', 'KNN', 'XT']
       infer_limit: 0.05
       infer_limit_batch_size: 10000
-      ag_args_ensemble: {'num_folds_parallel': 2}

modules/ml-pipeline/src/pipeline/configs/scenarios.yaml

@@ -1,13 +0,0 @@
-default:
-  scenarios:
-    input_dataclient_type: aws-s3
-    output_dataclient_type: local
-    scenario_data_filepaths:
-      # - s3://retrofit-data-dev/scenario_data/22-03-2024-19-20-09/recommendations_scoring_data.parquet
-      # - s3://retrofit-data-dev/scenario_data/24-03-2024-20-23-25/recommendations_scoring_data.parquet
-      # - s3://retrofit-data-dev/scenario_data/27-03-2024-11-38-15/recommendations_scoring_data.parquet
-      # - s3://retrofit-data-dev/scenario_data/26-05-2024-08-47-45/recommendations_scoring_data.parquet
-      # - s3://retrofit-data-dev/scenario_data/26-05-2024-10-44-53/recommendations_scoring_data.parquet
-      - s3://retrofit-data-dev/scenario_data/28-05-2024-19-22-41/recommendations_scoring_data.parquet
-    comparison_output_filepath: ./metrics/scenario_table.md
-    metrics_output_filepath: ./metrics/scenario_metrics.md

modules/ml-pipeline/src/pipeline/configs/settings.yaml

@@ -18,11 +18,13 @@ default:
   prepare_data:
     input_dataclient_type: aws-s3
     output_dataclient_type: local
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-03-22-18-56-53/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_with_differencing.parquet
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-25-08-36-36/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/floor_area_clean_test.parquet
-    # data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-26-10-31-39/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_without_differencing.parquet
-    data_filepath: s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset_test.parquet
-    train_proportion: 0.9
+    # data_filepath: s3://retrofit-data-dev/sap_change_model/dataset.parquet
+    data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet
+    train_proportion: 1
     output_train_filepath: ./data/prepared_data/train.parquet
     output_test_filepath: ./data/prepared_data/test.parquet
 
@@ -33,35 +35,9 @@ default:
       subsample_seed: 0
       target: sap_ending
       identifier_columns: ["uprn"]
-      # drop_columns: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending"]
+      drop_columns: ["heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending"]
-      drop_columns: [
+      # retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
-        "heat_demand_change", "carbon_change", "rdsap_change", "heat_demand_ending", "carbon_ending", "days_to_starting", "days_to_ending",
-        'number_habitable_rooms_starting', 'number_habitable_rooms_ending', 'number_heated_rooms_starting', 'number_heated_rooms_ending',
-        'number_habitable_rooms', 'number_heated_rooms']
       retain_features: null
-      # retain_features: ['uprn', 'sap_starting', 'hot_water_energy_eff_ending',
-      #  'mainheat_energy_eff_ending', 'constituency', 'roof_energy_eff_ending',
-      #  'walls_energy_eff_ending', 'secondheat_description_ending',
-      #  'property_type', 'mainheatc_energy_eff_ending', 'built_form',
-      #  'walls_insulation_thickness_ending', 'potential_energy_efficiency',
-      #  'transaction_type_ending',
-      #  'floor_thermal_transmittance_ending',
-      #  'low_energy_lighting_ending', 'heat_demand_starting',
-      #  'photo_supply_ending', 'carbon_starting',
-      #  'walls_thermal_transmittance_ending',
-      #  'roof_insulation_thickness_ending',
-      #  'total_floor_area_ending', 'number_open_fireplaces_ending',
-      #  'windows_energy_eff_ending',
-      #  'floor_height_ending',
-      #  'extension_count_ending',
-      #  'has_air_source_heat_pump_ending',
-      #  'charging_system_ending', 'construction_age_band', 'glazed_type_ending',
-      #  'roof_thermal_transmittance_ending',
-      #  'floor_insulation_thickness_ending', 'has_mains_gas_ending',
-      #  'estimated_perimeter_starting', 'energy_consumption_potential',
-      #  'environment_impact_potential', 'heater_type_ending',
-      #  'multi_glaze_proportion_ending',
-      #  'lighting_energy_eff_ending', 'fixed_lighting_outlets_count']
 
   generate_predictions:
     input_dataclient_type: local

modules/ml-pipeline/src/pipeline/core/DataClient.py

@@ -245,8 +245,7 @@ class LocalClient:
 
         save_methods = {
             ".parquet": self._save_parquet,
-            ".json": self._save_json,
+            ".json": self._save_json
-            ".md": self._save_md,
             # "": _save_directory(**save_config),
             # ADD MORE save_methods HERE
         }
@@ -295,10 +294,3 @@ class LocalClient:
         # Write the contents of the buffer to the local file
         with open(location, "wb") as f:
             f.write(buffer.getvalue())
-
-    def _save_md(self, obj: pd.DataFrame, location: str, save_config: dict):
-        """
-        Save object as markdown
-        """
-
-        obj.to_markdown(location, **save_config)

modules/ml-pipeline/src/pipeline/core/MLModels.py

@@ -25,7 +25,7 @@ def model_factory(model_type: str) -> MLModel:
     models = {
         "SKLearnLinearRegression": SKLearnLinearRegression(),
         "SKLearnSVMRegression": SKLearnSVMRegression(),
-        "AutogluonAutoML": AutogluonAutoML(),
+        "AutogluonAutoML": AutogluonAutoML()
         # ADD OTHER MODELS HERE
     }
 
@@ -151,7 +151,6 @@ class AutogluonAutoML:
         "excluded_model_types",
         "infer_limit",
         "infer_limit_batch_size",
-        "ag_args_ensemble",
     ]
 
     def load_model(self, path: Union[Path, str]) -> None:
@@ -208,7 +207,6 @@ class AutogluonAutoML:
             excluded_model_types=model_hyperparameters["excluded_model_types"],
             infer_limit=model_hyperparameters["infer_limit"],
             infer_limit_batch_size=model_hyperparameters["infer_limit_batch_size"],
-            ag_args_ensemble=model_hyperparameters["ag_args_ensemble"],
         )
 
     def predict(

modules/ml-pipeline/src/pipeline/dvc.lock

@@ -1,16 +1,5 @@
 schema: '2.0'
 stages:
-  startup_cleanup:
-    cmd: python 0_startup_cleanup.py
-    deps:
-    - path: 0_startup_cleanup.py
-      hash: md5
-      md5: b1b12f6b6393fbf8b83d23684df0a3d4
-      size: 1220
-    params:
-      configs/settings.yaml:
-        default.startup_cleanup.artefacts: ./data
-        default.startup_cleanup.metrics: ./metrics
   prepare_data:
     cmd: python 1_prepare_data.py
     deps:
@@ -28,29 +17,22 @@ stages:
         - carbon_ending
         - days_to_starting
         - days_to_ending
-        - number_habitable_rooms_starting
-        - number_habitable_rooms_ending
-        - number_heated_rooms_starting
-        - number_heated_rooms_ending
-        - number_habitable_rooms
-        - number_heated_rooms
         default.feature_processor.feature_processor_config.retain_features:
         default.feature_processor.feature_processor_config.subsample_amount:
         default.feature_processor.feature_processor_config.subsample_seed: 0
         default.feature_processor.feature_processor_config.target: sap_ending
         default.feature_processor.feature_processor_type: dataframe
-        default.prepare_data.data_filepath:
+        default.prepare_data.data_filepath: s3://retrofit-datalake-dev/dataset_with0perm_all.parquet
-          s3://retrofit-data-dev/sap_change_model/2024-05-28-19-08-25/dataset_rooms.parquet
         default.prepare_data.input_dataclient_type: aws-s3
         default.prepare_data.output_dataclient_type: local
         default.prepare_data.output_test_filepath: ./data/prepared_data/test.parquet
         default.prepare_data.output_train_filepath: ./data/prepared_data/train.parquet
-        default.prepare_data.train_proportion: 0.9
+        default.prepare_data.train_proportion: 1
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: f85c36a5dfd31a897538b3934d5fb997.dir
-      size: 45056059
+      size: 41375196
       nfiles: 2
   build_model:
     cmd: python 2_build_model.py
@@ -61,8 +43,8 @@ stages:
       size: 4820
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: f85c36a5dfd31a897538b3934d5fb997.dir
-      size: 45056059
+      size: 41375196
       nfiles: 2
     params:
       configs/build_model.yaml:
@@ -79,33 +61,32 @@ stages:
               output_filepath: ./data/model/allmodels/
               problem_type: regression
               eval_metric: mean_squared_error
-              time_limit: 1800
+              time_limit: 400
               presets: medium_quality
               excluded_model_types:
               - RF
+              - FASTAI
               - CAT
               - NN_TORCH
               - KNN
               - XT
               infer_limit: 0.05
               infer_limit_batch_size: 10000
-              ag_args_ensemble:
-                num_folds_parallel: 2
     outs:
     - path: data/fit_predictions/
       hash: md5
-      md5: d9c9afc05e8780db47c0548b19bf7d19.dir
+      md5: 991e6c55826953aa7c2be573369ec96f.dir
-      size: 3349989
+      size: 3574047
       nfiles: 1
     - path: data/model/
       hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
+      md5: f8a8b7462831bd46b1e2df47d73bb69d.dir
-      size: 773523079
+      size: 391430703
-      nfiles: 36
+      nfiles: 23
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: 2ff70a2a45813e1bcdf2ea3aa8e07d4a
+      md5: 35a66a845854cc6fee9dd10860e216bb
-      size: 224
+      size: 225
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
@@ -115,13 +96,13 @@ stages:
       size: 2464
     - path: data/model
       hash: md5
-      md5: 13c3100e1486c27a83a8a47491077842.dir
+      md5: f8a8b7462831bd46b1e2df47d73bb69d.dir
-      size: 773523079
+      size: 391430703
-      nfiles: 36
+      nfiles: 23
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: f85c36a5dfd31a897538b3934d5fb997.dir
-      size: 45056059
+      size: 41375196
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -133,8 +114,8 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      md5: 94b7381ac318b1ca18e0bc086778f7ce.dir
-      size: 463197
+      size: 626160
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -145,13 +126,13 @@ stages:
       size: 3484
     - path: data/predictions
       hash: md5
-      md5: 5d07bcebf3160a72bb18dfd79106e85c.dir
+      md5: 94b7381ac318b1ca18e0bc086778f7ce.dir
-      size: 463197
+      size: 626160
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 80c9e138146a1d96b9d16091c207e2e8.dir
+      md5: f85c36a5dfd31a897538b3934d5fb997.dir
-      size: 45056059
+      size: 41375196
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -161,30 +142,16 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: 3e08df02fd5c5d094bcf936e1338d596
+      md5: 4d8681f7c0f41f97be52d6b1ae039c5b
-      size: 223
+      size: 224
-  generate_scenerio_metrics:
+  startup_cleanup:
-    cmd: python 5_generate_scenarios.py
+    cmd: python 0_startup_cleanup.py
     deps:
-    - path: 5_generate_scenarios.py
+    - path: 0_startup_cleanup.py
       hash: md5
-      md5: 40506749fefd926d47c60ff5b16db307
+      md5: b1b12f6b6393fbf8b83d23684df0a3d4
-      size: 5337
+      size: 1220
     params:
-      configs/scenarios.yaml:
+      configs/settings.yaml:
-        default.scenarios:
+        default.startup_cleanup.artefacts: ./data
-          input_dataclient_type: aws-s3
+        default.startup_cleanup.metrics: ./metrics
-          output_dataclient_type: local
-          scenario_data_filepaths:
-          - s3://retrofit-data-dev/scenario_data/28-05-2024-19-22-41/recommendations_scoring_data.parquet
-          comparison_output_filepath: ./metrics/scenario_table.md
-          metrics_output_filepath: ./metrics/scenario_metrics.md
-    outs:
-    - path: metrics/scenario_metrics.md
-      hash: md5
-      md5: fa4d6d7bbd7818613800da5f8f37ea96
-      size: 363
-    - path: metrics/scenario_table.md
-      hash: md5
-      md5: d6baf100a1623cc2467c2f8221d314c9
-      size: 2133

modules/ml-pipeline/src/pipeline/dvc.yaml

@@ -71,17 +71,6 @@ stages:
     outs:
     - metrics/metrics.json
     always_changed: true
-  generate_scenerio_metrics:
-    cmd: python 5_generate_scenarios.py
-    deps:
-    - 5_generate_scenarios.py
-    params:
-    - configs/scenarios.yaml:
-      - default.scenarios
-    outs:
-    - metrics/scenario_table.md
-    - metrics/scenario_metrics.md
-    always_changed: true
 metrics:
   - metrics/metrics.json
   - metrics/fit_metrics.json

modules/ml-pipeline/src/pipeline/metrics/.gitignore

@@ -1,4 +1,2 @@
 /fit_metrics.json
 /metrics.json
-/scenario_table.md
-/scenario_metrics.md

modules/ml-pipeline/src/pipeline/requirements/predictions/requirements-dev.txt

@@ -1,7 +1,7 @@
 joblib==1.3.2
 boto3==1.28.17
-pandas==2.1.4
+pandas==1.5.3
-autogluon.tabular[all]==1.0.0
+autogluon==0.8.2
-dynaconf==3.2.1
+dynaconf==3.2.0
 pyarrow==13.0.0
 pre-commit==3.3.3

modules/ml-pipeline/src/pipeline/requirements/predictions/requirements.txt

@@ -1,7 +1,7 @@
 joblib==1.3.2
 boto3==1.28.17
-pandas==2.1.4
+pandas==1.5.3
-autogluon.tabular[all]==1.0.0
+autogluon==0.8.2
-dynaconf==3.2.1
+dynaconf==3.2.0
 pyarrow==13.0.0
 PyYAML==6.0.1

modules/ml-pipeline/src/pipeline/requirements/training/requirements-dev.txt

@@ -1,10 +1,9 @@
 joblib==1.3.2
 boto3==1.28.17
-pandas==2.1.4
+pandas==1.5.3
-autogluon.tabular[all]==1.0.0
+autogluon==0.8.2
-ray==2.6.3
+dynaconf==3.2.0
-dynaconf==3.2.1
+alibi==0.9.4
-alibi==0.9.5
 shap==0.42.1
 pyarrow==13.0.0
 pre-commit==3.3.3

modules/ml-pipeline/src/pipeline/requirements/training/requirements.txt

@@ -1,4 +1,4 @@
 boto3==1.28.41
-pandas==2.1.4
+pandas==1.5.3
-autogluon.tabular[all]==1.0.0
+autogluon==0.8.2
-dynaconf==3.2.1
+dynaconf==3.2.0

modules/ml-pipeline/src/pipeline/requirements/version_control/requirements.txt

@@ -1,4 +1,4 @@
-dvc==3.51.0
+dvc==3.36.0
-dvc-s3==3.2.0
+dvc-s3==3.0.1
-gto==1.7.1
+gto==1.6.1
 pyOpenSSL==23.3.0